Field of the Invention
The present invention relates to a device and a method for managing plant models that are models based on data concerning plants.
Priority is claimed on Japanese Patent Application No. 2013-084242, filed Apr. 12, 2013, the content of which is incorporated herein by reference.
Description of the Related Art
Highly-sophisticated automatic operation using plant models, which are models based on data concerning plants, has been implemented in various plants, such as petroleum refining plants, petrochemical plants, basic chemical plants, paper manufacturing plants, and iron manufacturing plants. For example, model predictive control, operational optimization, and the like, have been performed. Regarding the model predictive control, a soft sensor (or inferential sensor) that predicts a value of a product quality indicator in real time using plant models is provided, and the manipulation amount for a plant is determined based on the predicted value of the product quality indicator. Regarding the operational optimization, the optimal operational condition is determined using plant models under economic or safety restrictions. Conventionally, measurement of the above product quality indicator value has been time-consuming. Recently, the product quality indicator value can be predicted in real time by the above soft sensor using the plant models, thereby enabling highly-sophisticated automatic operation with high precision.
Japanese Patent Laid Open Publication No. 2004-280450 (hereinafter, “Patent Document 1”) discloses a device that automatically models plant models required for plant simulation. Specifically, the disclosed device includes: a modeling means that models behavior of a plant based on data to be used for modeling behavior of the plant; a input/output means that inputs/outputs the data to be used for modeling behavior of the plant, and the model resulting from the modeling; and a simulation means that simulates behavior of the plant based on the model for simulating behavior of the plant, which is modeled by the modeling means.
This device models behavior of the plant using a combination of an unsupervised learning algorithm such as a self-organizing map, a highly-expressive model such as an RBF (radial basis function) network, a method of optimizing parameters based on the least square criterion. Additionally, the behavior of the plant modeled in this manner is simulated based on a model for simulating that behavior of the plant, and update of the model is performed in accordance with a result of the simulation.
It seems to be possible to easily and freely model a desired plant model using the device disclosed in Patent Document 1. When a precise plant model is actually generated, however, work such as carefully considering parameters based on highly-specialized knowledge is required, thereby increasing the burden on a system user who generates a plant model.
For example, in a process of removing inadequate data from data required for generating a plant model, the system user has to define criteria for normal data (labeling). Generally, the labeling is difficult to perform in non-linear modeling. This is because a non-linear model has a high degree-of-freedom in the shapes of functions, and it is difficult to determine whether to use data out of the distribution of mainstream data in order to generate a plant model, or to remove the data (outlier determination).
For example, in a process using the aforementioned RBF network, a system user has to appropriately set the number of neurons. However, it is extremely difficult to determine the adequate number of neurons without careful consideration. Here, in a case where the set number of neurons is too large, a phenomenon called “over-fitting” occurs. Thus, a plant model reflecting generally-meaningless noise-level information is obtained, thereby greatly reducing the prediction performance of the aforementioned soft sensor or the like. On the other hand, in a case where the set number of neurons is too small, a phenomenon called “under-fitting” occurs. Thus, a plant model sufficiently reflecting the property of the plant cannot be obtained.
In a case where an adequate plant model is not generated, preferable prediction performance cannot be achieved after operation is initiated, thereby frequently requiring reconfiguration of a plant model. As explained above, a plant automatically operates based on the product quality indicator value predicted by the soft sensor. For this reason, in the above situation frequently requiring reconfiguration of a plant model, the availability of the plant is decreased, thereby finally causing a reduction in production efficiency.